Optimizing product recommendations for millions of merchants

Abstract

At Shopify, we serve product recommendations to customers across millions of merchants’ online stores. It is a challenge to provide optimized recommendations to all of these independent merchants; one model might lead to an overall improvement in our metrics on aggregate, but significantly degrade recommendations for some stores. To ensure we provide high quality recommendations to all merchant segments, we develop several models that work best in different situations as determined in offline evaluation. Learning which strategy best works for a given segment also allows us to start off new stores with good recommendations, without necessarily needing to rely on an individual store amassing large amounts of traffic. In production, the system will start out with the best strategy for a given merchant, and then adjust to the current environment using multi-armed bandits. Collectively, this methodology allows us to optimize the types of recommendations served on each store.